Triple
T14644490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | De La Salle University |
E343810
|
entity |
| Predicate | athleticsNickname |
P55
|
FINISHED |
| Object |
Lady Archers
Lady Archers is the moniker for the women’s varsity sports teams of De La Salle University in the Philippines.
|
E1111511
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Lady Archers | Statement: [De La Salle University, athleticsNickname, Lady Archers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lady Archers Context triple: [De La Salle University, athleticsNickname, Lady Archers]
-
A.
The Archer
"The Archer" is a reflective, synth-pop ballad by Taylor Swift that explores vulnerability, self-doubt, and emotional introspection.
-
B.
The Green Archer
The Green Archer is a mystery novel by British writer Edgar Wallace, centered on a series of crimes linked to a legendary, hooded archer haunting a gloomy English estate.
-
C.
Black Arrows
Black Arrows was the famous aerobatic display team of No. 111 Squadron RAF, renowned for its precision jet formation flying during the late 1950s and early 1960s.
-
D.
Lady Caine
Lady Caine is Shakira Caine, a Guyanese-British former fashion model and actress best known as the wife of English actor Sir Michael Caine.
-
E.
Jane Feather
Jane Feather is a lyricist known for her work on the blues song "How Blue Can You Get."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lady Archers Triple: [De La Salle University, athleticsNickname, Lady Archers]
Generated description
Lady Archers is the moniker for the women’s varsity sports teams of De La Salle University in the Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lady Archers Target entity description: Lady Archers is the moniker for the women’s varsity sports teams of De La Salle University in the Philippines.
-
A.
The Archer
"The Archer" is a reflective, synth-pop ballad by Taylor Swift that explores vulnerability, self-doubt, and emotional introspection.
-
B.
The Green Archer
The Green Archer is a mystery novel by British writer Edgar Wallace, centered on a series of crimes linked to a legendary, hooded archer haunting a gloomy English estate.
-
C.
Black Arrows
Black Arrows was the famous aerobatic display team of No. 111 Squadron RAF, renowned for its precision jet formation flying during the late 1950s and early 1960s.
-
D.
Lady Caine
Lady Caine is Shakira Caine, a Guyanese-British former fashion model and actress best known as the wife of English actor Sir Michael Caine.
-
E.
Jane Feather
Jane Feather is a lyricist known for her work on the blues song "How Blue Can You Get."
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4ea6d8481908e6331ca173c646b |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5d5d05481908dbb23392c05d23b |
completed | May 8, 2026, 12:23 p.m. |
| NEDg | Description generation | batch_69fdd74cc4048190bae5f75d922c9618 |
completed | May 8, 2026, 12:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdd7bd20748190b9145ef14ce2759b |
completed | May 8, 2026, 12:31 p.m. |
Created at: April 10, 2026, 1:26 a.m.