Triple
T9875394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FRLEH |
E240059
|
entity |
| Predicate | hasLastThreeLettersMeaning |
P91001
|
FINISHED |
| Object | Le Havre location code |
—
|
LITERAL 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: Le Havre location code | Statement: [FRLEH, hasLastThreeLettersMeaning, Le Havre location code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLastThreeLettersMeaning Context triple: [FRLEH, hasLastThreeLettersMeaning, Le Havre location code]
-
A.
hasThreeLetterForm
Indicates that an entity’s written or symbolic form consists of exactly three letters.
-
B.
hasThirdLetter
Indicates that a string or sequence possesses a specific character or element in the third position.
-
C.
hasLettersFor
Indicates that one entity possesses or contains written correspondence intended for another entity.
-
D.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
-
E.
hasLetterBy
Indicates that an entity possesses or is associated with a letter authored or sent by another entity.
- F. None of above. chosen
Provenance (4 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3f9d82c81908afb4977ce4e3e4a |
completed | April 2, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69cd3581a9688190a00cef4c3eebb0ae |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 8:37 p.m.