Triple
T10785939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cluett Peabody & Company |
E254450
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object | Arrow |
E885827
|
NE FINISHED |
How this triple was built (2 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: Arrow | Statement: [Cluett Peabody & Company, hasBrand, Arrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arrow Context triple: [Cluett Peabody & Company, hasBrand, Arrow]
-
A.
Arrow
Arrow is the English translation of "Freccia," the nickname of the Italian World War II fighter aircraft Fiat G.50.
-
B.
Arrow
Arrow is a popular American superhero television series based on the DC Comics character Green Arrow, known for launching the interconnected "Arrowverse" franchise.
-
C.
Arrow
Arrow is a common English surname borne by various individuals, including the influential economist Kenneth Arrow.
-
D.
Arrow
Arrow is a regional passenger rail service brand used for trains operating between San Bernardino and Redlands in Southern California.
-
E.
Arrow
chosen
Arrow is a long-established American clothing brand best known for its men’s dress shirts and formalwear.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732d44b488190a9f3ab9b177e737a |
completed | April 9, 2026, 5:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de84ddd6f48190a11eae9233d3ef9f |
completed | April 14, 2026, 6:18 p.m. |
Created at: April 8, 2026, 9:17 p.m.