Triple
T25171024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XTSE |
E630321
|
entity |
| Predicate | hasSegmentScope |
P397
|
FINISHED |
| Object | Main market |
—
|
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: Main market | Statement: [XTSE, hasSegmentScope, Main market]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSegmentScope Context triple: [XTSE, hasSegmentScope, Main market]
-
A.
hasSegmentWith
Indicates that an entity contains or includes at least one segment that satisfies a specified condition or matches a given segment.
-
B.
hasScope
chosen
Indicates that one entity defines, limits, or encompasses the range, extent, or applicability within which another entity operates or is valid.
-
C.
hasSegmentFrom
Indicates that something includes or contains a segment that originates from or is derived from another specified source.
-
D.
hasSegmentBasedOn
Indicates that one segment is derived from, modeled after, or constructed using another segment as its basis.
-
E.
hasSegmentOn
Indicates that one entity includes or occupies a specific segment or portion on another entity (such as a line, path, or sequence).
- F. None of above.
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_69e75a87c9b88190ab60731902a99750 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f67c9fe7b48190b79b4041357edb49 |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 21, 2026, 12:20 p.m.