Triple
T14494078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hôtel-Dieu de Tonnerre |
E359442
|
entity |
| Predicate | hasChapelLocation |
P114449
|
FINISHED |
| Object | end of the main hall |
—
|
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: end of the main hall | Statement: [Hôtel-Dieu de Tonnerre, hasChapelLocation, end of the main hall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChapelLocation Context triple: [Hôtel-Dieu de Tonnerre, hasChapelLocation, end of the main hall]
-
A.
hasChapelNearby
Indicates that one entity is located close to or in the vicinity of a chapel.
-
B.
hasChapels
Indicates that one entity contains, includes, or is associated with one or more chapels.
-
C.
hasChapelUse
Indicates that something is used, designated, or functions as a chapel or for chapel-related purposes.
-
D.
hasChapelHonoring
Indicates that one entity contains or includes a chapel dedicated to honoring another entity.
-
E.
hasChapelCountApprox
Indicates an approximate number of chapels associated with an 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de930f34d08190b4b30e54e2f702ed |
completed | April 14, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69de5c4ccba08190a988bfda0bc9f5cb |
completed | April 14, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69de5fb4de14819092acdecbd201d672 |
completed | April 14, 2026, 3:39 p.m. |
Created at: April 10, 2026, 1:21 a.m.